Title :
Detecting changes of opinion from customer reviews
Author :
Li-Chen Cheng ; Zhi-Han Ke ; Bang-Min Shiue
Author_Institution :
Dept. of Comput. Sci. & Inf. Manage., Soochow Univ., Taipei, Taiwan
Abstract :
With the flourishing of the world wide web, the online customer review process is becoming more and more useful and important as an information resource for people. As a result, opinion mining research for analysis of opinion data on the web has recently become a popular topic. Most previous studies have used Pointwise Mutual Information (PMI) to predict the opinion (positive or negative). This study first proposes a method which combines the associative classification methodology with the overall rating to discover the relation of the features. By the way, a framework is also proposed to discover those changes of opinion that can identify the users´ opinion of a product. Experimental results, in mining ipad review information, demonstrate the effectiveness of the proposed approach. The data set used contains actual WOM information from which to study the dynamic patterns of users´ changing opinions. The summarized results can help consumers and marketing managers to make decision.
Keywords :
Internet; Web sites; customer satisfaction; data mining; electronic commerce; pattern classification; World Wide Web; associative classification methodology; data set; iPad review information; information resource; marketing managers; online customer review process; opinion mining research; pointwise mutual information; Association rules; Batteries; Computer science; Feature extraction; Semantics; Software; change mining; data mining; group decision making; maximum consensus sequence; pairwise comparisons;
Conference_Titel :
Fuzzy Systems and Knowledge Discovery (FSKD), 2011 Eighth International Conference on
Conference_Location :
Shanghai
Print_ISBN :
978-1-61284-180-9
DOI :
10.1109/FSKD.2011.6019917